Accurate road extraction from LiDAR data remains challenging, as the absence of spectral cues or spatial heterogeneity increases model uncertainty. This study introduces a comparative, entropy-driven framework for evaluating the performance and reliability of road asset detection using three supervised machine learning algorithms—XGBoost, Random Forest (RF), and Support Vector Machine (SVM). Using a high-density aerial point cloud, a reproducible computational pipeline was developed to select reliable machine learning methods for road asset mapping. Beyond traditional accuracy metrics (Overall Accuracy, F1-score, and Kappa coefficient), uncertainty-based evaluation of the outputs has been conducted using KPIs of entropy and sensitivity to training sets to quantify model reliability and spatial instability. Results reveal that the inclusion of RGB significantly reduces entropy across all models. XGBoost achieved the lowest mean entropy (0.084–0.143) and the most consistent probabilistic behaviour, reflecting confident and well-calibrated model. SVM, while statistically the most accurate (OA and Kappa > 0.97), exhibited higher local entropy (≈ 0.23–0.26), implying precise yet less certain classification. RF demonstrated the highest entropy (≈ 0.65–0.70) and the greatest variability, underscoring its sensitivity to feature noise. Under the WOR configuration, mean entropy rose markedly—most for RF_WOR (≈ 0.93) and moderately for SVM_WOR (≈ 0.39)—while XGBoost retained low uncertainty. Spatial entropy maps further highlighted that uncertainty concentrates along road edges with RGB data but expands diffusely under WOR conditions, emphasizing the role of spectral–spatial synergy in constraining ambiguity. Entropy-based evaluation provided insights beyond conventional accuracy metrics, revealing paradoxes between correctness and confidence.